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ICF框架下的康复大数据标准研究

田益凡 叶海燕 刘叶 程亚宁 殷瑞雪 吕雪丽 陈迪

中国康复理论与实践2024,Vol.30Issue(11):1262-1271,10.
中国康复理论与实践2024,Vol.30Issue(11):1262-1271,10.DOI:10.3969/j.issn.1006-9771.2024.11.004

ICF框架下的康复大数据标准研究

Rehabilitation big data standards under ICF framework

田益凡 1叶海燕 1刘叶 1程亚宁 1殷瑞雪 1吕雪丽 1陈迪2

作者信息

  • 1. 中国康复科学所康复信息研究部,北京市 100068
  • 2. 中国康复科学所康复信息研究部,北京市 100068||世界卫生组织国际分类家族中国合作中心,北京市 100068
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摘要

Abstract

Objective To explore and organize the standards of rehabilitation big data. Methods The connotation and extension of rehabilitation big data were discussed based on International Classification of Functioning,Disability and Health(ICF)framework.Referring to the documents of Guidance on the analysis and use of routine health information systems rehabilitation module,Rehabilitation in health systems:guide for action,Rehabilitation indicator menu:a tool accompanying the Framework for Rehabilitation Monitoring and Evaluation(FRAME),and Data quality assurance.Module 1.Framework and metrics,the sources,patterns,clas-sification systems and coding standards were discussed under the ICF theory,and the metadata standards were ex-plored.The application and management of rehabilitation big data standards were discussed according to Nation-al Health Medical Big Data Standards,Security and Service Management Measures(Trial). Results The rehabilitation big data included rehabilitation service data and personal health data,coming from population-based and institution-based data,covering macro,meso and micro levels.The pattern of rehabilitation data flow corresponded to the interaction and source of the entire process of rehabilitation service,to organize and manage rehabilitation big data.The classification system included object classes,object feature classes,participant role classes,relationship classes,and activity and event classes,each of which was further subdivided into subcatego-ries to cover the entities,features,roles,relationships and activities involved in the rehabilitation process.The metadata standards included three levels:core,general and specialized metadata,ensuring standardized manage-ment,sharing and interoperability of rehabilitation data. Conclusion This study delves into the standardization of rehabilitation big data based on the ICF framework,encompass-ing multiple dimensions such as the connotation and extension of rehabilitation big data,data sources,data mod-els,classification systems,coding standards,and metadata standards.The construction of a rehabilitation big data standard system involves standardization efforts in various aspects,including data content,data structure,data coding,and metadata.These standards not only adhere to the norms of data flow,but also take into account the complexity of data composition.This system aligns with health big data standards,ensuring data consistency,ac-curacy,and interoperability,thus providing a foundation for effective exchange and comparison between different data sources.The establishment of a rehabilitation big data standard system not only ensures the standardized pro-cessing of rehabilitation big data,but also lays a solid foundation for effective exchange between rehabilitation big data and other health data,as well as for the widespread application of rehabilitation big data.This provides crucial support for improving the quality and efficiency of rehabilitation services,ensuring that patients receive appropriate care,rehabilitation and support.It holds significant theoretical and practical implications for promot-ing the development of the rehabilitation field.

关键词

康复大数据/数据标准化/国际功能、残疾和健康分类/数据质量/数据管理

Key words

rehabilitation big data/data standardization/International Classification of Functioning,Disability and Health/data quality/data management

分类

医药卫生

引用本文复制引用

田益凡,叶海燕,刘叶,程亚宁,殷瑞雪,吕雪丽,陈迪..ICF框架下的康复大数据标准研究[J].中国康复理论与实践,2024,30(11):1262-1271,10.

基金项目

中国康复科学所中央级公益性科研院所基本科研业务费项目(No.2021CZ-14) The Fundamental Research Funds for Central Public Welfare Research Institutes,conducted by China Rehabilitation Science Institute(No.2021CZ-14) (No.2021CZ-14)

中国康复理论与实践

OA北大核心CSTPCD

1006-9771

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